/**
 * Created by Sarah on 05/02/14.
 */
/*
 * NeuQuant Neural-Net Quantization Algorithm
 * ------------------------------------------
 *
 * Copyright (c) 1994 Anthony Dekker
 *
 * NEUQUANT Neural-Net quantization algorithm by Anthony Dekker, 1994. See
 * "Kohonen neural networks for optimal colour quantization" in "Network:
 * Computation in Neural Systems" Vol. 5 (1994) pp 351-367. for a discussion of
 * the algorithm.
 *
 * Any party obtaining a copy of these files from the author, directly or
 * indirectly, is granted, free of charge, a full and unrestricted irrevocable,
 * world-wide, paid up, royalty-free, nonexclusive right and license to deal in
 * this software and documentation files (the "Software"), including without
 * limitation the rights to use, copy, modify, merge, publish, distribute,
 * sublicense, and/or sell copies of the Software, and to permit persons who
 * receive copies from any such party to do so, with the only requirement being
 * that this copyright notice remain intact.
 */

/*
 * This class handles Neural-Net quantization algorithm
 * @author Kevin Weiner (original Java version - kweiner@fmsware.com)
 * @author Thibault Imbert (AS3 version - bytearray.org)
 * @version 0.1 AS3 implementation
 */

//import flash.utils.ByteArray;

NeuQuant = function()
{
    var exports = {};
    /*private_static*/ var netsize/*int*/ = 256; /* number of colours used */

    /* four primes near 500 - assume no image has a length so large */
    /* that it is divisible by all four primes */

    /*private_static*/ var prime1/*int*/ = 499;
    /*private_static*/ var prime2/*int*/ = 491;
    /*private_static*/ var prime3/*int*/ = 487;
    /*private_static*/ var prime4/*int*/ = 503;
    /*private_static*/ var minpicturebytes/*int*/ = (3 * prime4);

    /* minimum size for input image */
    /*
     * Program Skeleton ---------------- [select samplefac in range 1..30] [read
     * image from input file] pic = (unsigned char*) malloc(3*width*height);
     * initnet(pic,3*width*height,samplefac); learn(); unbiasnet(); [write output
     * image header, using writecolourmap(f)] inxbuild(); write output image using
     * inxsearch(b,g,r)
     */

    /*
     * Network Definitions -------------------
     */

    /*private_static*/ var maxnetpos/*int*/ = (netsize - 1);
    /*private_static*/ var netbiasshift/*int*/ = 4; /* bias for colour values */
    /*private_static*/ var ncycles/*int*/ = 100; /* no. of learning cycles */

    /* defs for freq and bias */
    /*private_static*/ var intbiasshift/*int*/ = 16; /* bias for fractions */
    /*private_static*/ var intbias/*int*/ = (1 << intbiasshift);
    /*private_static*/ var gammashift/*int*/ = 10; /* gamma = 1024 */
    /*private_static*/ var gamma/*int*/ = (1 << gammashift);
    /*private_static*/ var betashift/*int*/ = 10;
    /*private_static*/ var beta/*int*/ = (intbias >> betashift); /* beta = 1/1024 */
    /*private_static*/ var betagamma/*int*/ = (intbias << (gammashift - betashift));

    /* defs for decreasing radius factor */
    /*private_static*/ var initrad/*int*/ = (netsize >> 3); /*
 * for 256 cols, radius
 * starts
 */

    /*private_static*/ var radiusbiasshift/*int*/ = 6; /* at 32.0 biased by 6 bits */
    /*private_static*/ var radiusbias/*int*/ = (1 << radiusbiasshift);
    /*private_static*/ var initradius/*int*/ = (initrad * radiusbias); /*
 * and
 * decreases
 * by a
 */

    /*private_static*/ var radiusdec/*int*/ = 30; /* factor of 1/30 each cycle */

    /* defs for decreasing alpha factor */
    /*private_static*/ var alphabiasshift/*int*/ = 10; /* alpha starts at 1.0 */
    /*private_static*/ var initalpha/*int*/ = (1 << alphabiasshift);
    /*private*/ var alphadec/*int*/ /* biased by 10 bits */

    /* radbias and alpharadbias used for radpower calculation */
    /*private_static*/ var radbiasshift/*int*/ = 8;
    /*private_static*/ var radbias/*int*/ = (1 << radbiasshift);
    /*private_static*/ var alpharadbshift/*int*/ = (alphabiasshift + radbiasshift);

    /*private_static*/ var alpharadbias/*int*/ = (1 << alpharadbshift);

    /*
     * Types and Global Variables --------------------------
     */

    /*private*/ var thepicture/*ByteArray*//* the input image itself */
    /*private*/ var lengthcount/*int*/; /* lengthcount = H*W*3 */
    /*private*/ var samplefac/*int*/; /* sampling factor 1..30 */

    // typedef int pixel[4]; /* BGRc */
    /*private*/ var network/*Array*/; /* the network itself - [netsize][4] */
    /*protected*/ var netindex/*Array*/ = new Array();

    /* for network lookup - really 256 */
    /*private*/ var bias/*Array*/ = new Array();

    /* bias and freq arrays for learning */
    /*private*/ var freq/*Array*/ = new Array();
    /*private*/ var radpower/*Array*/ = new Array();

    var NeuQuant = exports.NeuQuant = function NeuQuant(thepic/*ByteArray*/, len/*int*/, sample/*int*/)
    {

        var i/*int*/;
        var p/*Array*/;

        thepicture = thepic;
        lengthcount = len;
        samplefac = sample;

        network = new Array(netsize);

        for (i = 0; i < netsize; i++)
        {

            network[i] = new Array(4);
            p = network[i];
            p[0] = p[1] = p[2] = (i << (netbiasshift + 8)) / netsize;
            freq[i] = intbias / netsize; /* 1/netsize */
            bias[i] = 0;
        }

    }

    var colorMap = function colorMap()/*ByteArray*/
    {

        var map/*ByteArray*/ = [];
        var index/*Array*/ = new Array(netsize);
        for (var i/*int*/ = 0; i < netsize; i++)
            index[network[i][3]] = i;
        var k/*int*/ = 0;
        for (var l/*int*/ = 0; l < netsize; l++) {
            var j/*int*/ = index[l];
            map[k++] = (network[j][0]);
            map[k++] = (network[j][1]);
            map[k++] = (network[j][2]);
        }
        return map;

    }

    /*
     * Insertion sort of network and building of netindex[0..255] (to do after
     * unbias)
     * -------------------------------------------------------------------------------
     */

    var inxbuild = function inxbuild()/*void*/
    {

        var i/*int*/;
        var j/*int*/;
        var smallpos/*int*/;
        var smallval/*int*/;
        var p/*Array*/;
        var q/*Array*/;
        var previouscol/*int*/
        var startpos/*int*/

        previouscol = 0;
        startpos = 0;
        for (i = 0; i < netsize; i++)
        {

            p = network[i];
            smallpos = i;
            smallval = p[1]; /* index on g */
            /* find smallest in i..netsize-1 */
            for (j = i + 1; j < netsize; j++)
            {
                q = network[j];
                if (q[1] < smallval)
                { /* index on g */

                    smallpos = j;
                    smallval = q[1]; /* index on g */
                }
            }

            q = network[smallpos];
            /* swap p (i) and q (smallpos) entries */

            if (i != smallpos)
            {

                j = q[0];
                q[0] = p[0];
                p[0] = j;
                j = q[1];
                q[1] = p[1];
                p[1] = j;
                j = q[2];
                q[2] = p[2];
                p[2] = j;
                j = q[3];
                q[3] = p[3];
                p[3] = j;

            }

            /* smallval entry is now in position i */

            if (smallval != previouscol)

            {

                netindex[previouscol] = (startpos + i) >> 1;

                for (j = previouscol + 1; j < smallval; j++) netindex[j] = i;

                previouscol = smallval;
                startpos = i;

            }

        }

        netindex[previouscol] = (startpos + maxnetpos) >> 1;
        for (j = previouscol + 1; j < 256; j++) netindex[j] = maxnetpos; /* really 256 */

    }

    /*
     * Main Learning Loop ------------------
     */

    var learn = function learn()/*void*/

    {

        var i/*int*/;
        var j/*int*/;
        var b/*int*/;
        var g/*int*/
        var r/*int*/;
        var radius/*int*/;
        var rad/*int*/;
        var alpha/*int*/;
        var step/*int*/;
        var delta/*int*/;
        var samplepixels/*int*/;
        var p/*ByteArray*/;
        var pix/*int*/;
        var lim/*int*/;

        if (lengthcount < minpicturebytes) samplefac = 1;

        alphadec = 30 + ((samplefac - 1) / 3);
        p = thepicture;
        pix = 0;
        lim = lengthcount;
        samplepixels = lengthcount / (3 * samplefac);
        delta = (samplepixels / ncycles) | 0;
        alpha = initalpha;
        radius = initradius;

        rad = radius >> radiusbiasshift;
        if (rad <= 1) rad = 0;

        for (i = 0; i < rad; i++) radpower[i] = alpha * (((rad * rad - i * i) * radbias) / (rad * rad));


        if (lengthcount < minpicturebytes) step = 3;

        else if ((lengthcount % prime1) != 0) step = 3 * prime1;

        else

        {

            if ((lengthcount % prime2) != 0) step = 3 * prime2;

            else

            {

                if ((lengthcount % prime3) != 0) step = 3 * prime3;

                else step = 3 * prime4;

            }

        }

        i = 0;

        while (i < samplepixels)

        {

            b = (p[pix + 0] & 0xff) << netbiasshift;
            g = (p[pix + 1] & 0xff) << netbiasshift;
            r = (p[pix + 2] & 0xff) << netbiasshift;
            j = contest(b, g, r);

            altersingle(alpha, j, b, g, r);

            if (rad != 0) alterneigh(rad, j, b, g, r); /* alter neighbours */

            pix += step;

            if (pix >= lim) pix -= lengthcount;

            i++;

            if (delta == 0) delta = 1;

            if (i % delta == 0)

            {

                alpha -= alpha / alphadec;
                radius -= radius / radiusdec;
                rad = radius >> radiusbiasshift;

                if (rad <= 1) rad = 0;

                for (j = 0; j < rad; j++) radpower[j] = alpha * (((rad * rad - j * j) * radbias) / (rad * rad));

            }

        }

    }

    /*
     ** Search for BGR values 0..255 (after net is unbiased) and return colour
     * index
     * ----------------------------------------------------------------------------
     */

    var map = exports.map = function map(b/*int*/, g/*int*/, r/*int*/)/*int*/

    {

        var i/*int*/;
        var j/*int*/;
        var dist/*int*/
        var a/*int*/;
        var bestd/*int*/;
        var p/*Array*/;
        var best/*int*/;

        bestd = 1000; /* biggest possible dist is 256*3 */
        best = -1;
        i = netindex[g]; /* index on g */
        j = i - 1; /* start at netindex[g] and work outwards */

        while ((i < netsize) || (j >= 0))

        {

            if (i < netsize)

            {

                p = network[i];

                dist = p[1] - g; /* inx key */

                if (dist >= bestd) i = netsize; /* stop iter */

                else

                {

                    i++;

                    if (dist < 0) dist = -dist;

                    a = p[0] - b;

                    if (a < 0) a = -a;

                    dist += a;

                    if (dist < bestd)

                    {

                        a = p[2] - r;

                        if (a < 0) a = -a;

                        dist += a;

                        if (dist < bestd)

                        {

                            bestd = dist;
                            best = p[3];

                        }

                    }

                }

            }

            if (j >= 0)
            {

                p = network[j];

                dist = g - p[1]; /* inx key - reverse dif */

                if (dist >= bestd) j = -1; /* stop iter */

                else
                {

                    j--;
                    if (dist < 0) dist = -dist;
                    a = p[0] - b;
                    if (a < 0) a = -a;
                    dist += a;

                    if (dist < bestd)

                    {

                        a = p[2] - r;
                        if (a < 0)a = -a;
                        dist += a;
                        if (dist < bestd)
                        {
                            bestd = dist;
                            best = p[3];
                        }

                    }

                }

            }

        }

        return (best);

    }

    var process = exports.process = function process()/*ByteArray*/
    {

        learn();
        unbiasnet();
        inxbuild();
        return colorMap();

    }

    /*
     * Unbias network to give byte values 0..255 and record position i to prepare
     * for sort
     * -----------------------------------------------------------------------------------
     */

    var unbiasnet = function unbiasnet()/*void*/

    {

        var i/*int*/;
        var j/*int*/;

        for (i = 0; i < netsize; i++)
        {
            network[i][0] >>= netbiasshift;
            network[i][1] >>= netbiasshift;
            network[i][2] >>= netbiasshift;
            network[i][3] = i; /* record colour no */
        }

    }

    /*
     * Move adjacent neurons by precomputed alpha*(1-((i-j)^2/[r]^2)) in
     * radpower[|i-j|]
     * ---------------------------------------------------------------------------------
     */

    var alterneigh = function alterneigh(rad/*int*/, i/*int*/, b/*int*/, g/*int*/, r/*int*/)/*void*/

    {

        var j/*int*/;
        var k/*int*/;
        var lo/*int*/;
        var hi/*int*/;
        var a/*int*/;
        var m/*int*/;

        var p/*Array*/;

        lo = i - rad;
        if (lo < -1) lo = -1;

        hi = i + rad;

        if (hi > netsize) hi = netsize;

        j = i + 1;
        k = i - 1;
        m = 1;

        while ((j < hi) || (k > lo))

        {

            a = radpower[m++];

            if (j < hi)

            {

                p = network[j++];

                try {

                    p[0] -= (a * (p[0] - b)) / alpharadbias;
                    p[1] -= (a * (p[1] - g)) / alpharadbias;
                    p[2] -= (a * (p[2] - r)) / alpharadbias;

                } catch (e/*Error*/) {} // prevents 1.3 miscompilation

            }

            if (k > lo)

            {

                p = network[k--];

                try
                {

                    p[0] -= (a * (p[0] - b)) / alpharadbias;
                    p[1] -= (a * (p[1] - g)) / alpharadbias;
                    p[2] -= (a * (p[2] - r)) / alpharadbias;

                } catch (e/*Error*/) {}

            }

        }

    }

    /*
     * Move neuron i towards biased (b,g,r) by factor alpha
     * ----------------------------------------------------
     */

    var altersingle = function altersingle(alpha/*int*/, i/*int*/, b/*int*/, g/*int*/, r/*int*/)/*void*/
    {

        /* alter hit neuron */
        var n/*Array*/ = network[i];
        n[0] -= (alpha * (n[0] - b)) / initalpha;
        n[1] -= (alpha * (n[1] - g)) / initalpha;
        n[2] -= (alpha * (n[2] - r)) / initalpha;

    }

    /*
     * Search for biased BGR values ----------------------------
     */

    var contest = function contest(b/*int*/, g/*int*/, r/*int*/)/*int*/
    {

        /* finds closest neuron (min dist) and updates freq */
        /* finds best neuron (min dist-bias) and returns position */
        /* for frequently chosen neurons, freq[i] is high and bias[i] is negative */
        /* bias[i] = gamma*((1/netsize)-freq[i]) */

        var i/*int*/;
        var dist/*int*/;
        var a/*int*/;
        var biasdist/*int*/;
        var betafreq/*int*/;
        var bestpos/*int*/;
        var bestbiaspos/*int*/;
        var bestd/*int*/;
        var bestbiasd/*int*/;
        var n/*Array*/;

        bestd = ~(1 << 31);
        bestbiasd = bestd;
        bestpos = -1;
        bestbiaspos = bestpos;

        for (i = 0; i < netsize; i++)

        {

            n = network[i];
            dist = n[0] - b;

            if (dist < 0) dist = -dist;

            a = n[1] - g;

            if (a < 0) a = -a;

            dist += a;

            a = n[2] - r;

            if (a < 0) a = -a;

            dist += a;

            if (dist < bestd)

            {

                bestd = dist;
                bestpos = i;

            }

            biasdist = dist - ((bias[i]) >> (intbiasshift - netbiasshift));

            if (biasdist < bestbiasd)

            {

                bestbiasd = biasdist;
                bestbiaspos = i;

            }

            betafreq = (freq[i] >> betashift);
            freq[i] -= betafreq;
            bias[i] += (betafreq << gammashift);

        }

        freq[bestpos] += beta;
        bias[bestpos] -= betagamma;
        return (bestbiaspos);

    }

    NeuQuant.apply(this, arguments);
    return exports;
}